Vishwanath Sindagi

PhD Student, ECE Dept., Johns Hopkins University

    Email: vishwanathsindagi@jhu.edu

I am a 4th year PhD student in Dept. Of Electrical & Computer Engineering at Johns Hopkins University. I am being advised by Prof. Vishal M Patel. Prior to joining Johns Hopkins, I worked for Samsung R&D Institute-Bangalore. I graduated from IIIT-Bangalore with a Master's degree in Information Technology.

My research is on computer vision and machine learning with a specific focus on crowd analytics, face detection, applications of generative modeling, domain adaptation and low-level vision.

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Research

Image-based Crowd Analytics

Single image-based crowd analytics is plagued with several issues such as occlusion, variations in scale and appearance. We develop novel CNN-based methods such as contextual pyramid CNNs, multi-level fusion scheme, uncertainty aware residual learning, etc to address several of these issues.

Image Restoration

Weather conditions such as rain and haze corrupt the image quality, resulting in deterioration of downstream computer vision algorithms. We design specialized CNN networks and loss functions that leverage mathematical models of image degradation to tackle such degradations.

Weather conditions such as rain and haze corrupt the image quality, resulting in deterioration of downstream computer vision algorithms. We design specialized CNN networks and loss functions that leverage mathematical models of image degradation to tackle such degradations.

Image De-raining using a Conditional Generative Adversarial Network Joint Transmission Map Estimation and Dehazing using Deep Networks Multi-scale Single Image Dehazing using Perceptual Pyramid Deep Network

Domain Adaptation

Deep networks perform poorly when used on samples that have a distributional shift as compared to that of training samples. We develop unsupervised domain adaptation techniques to adapt networks to different domains for several applications like detection, crowd counting, etc.

Deep networks perform poorly when used on samples that have a distributional shift as compared to that of training samples. We develop unsupervised domain adaptation techniques to adapt networks to different domains for several applications like detection, crowd counting, etc.

Prior-based Domain Adaptive Object Detection for Adverse Weather Conditions Domain Adaptation for Automatic OLED Panel Defect Detection using Adaptive Support Vector Data Description

Object Detection

Object Detection is a critical component in computer vision pipelines like face recognition and autonomous navigation algorithms. We focus on several aspects such as tiny object detection, domain adaptive object detection and fusion architectures for 3D object detection.

Object Detection is a critical component in computer vision pipelines like face recognition and autonomous navigation algorithms. We focus on several aspects such as tiny object detection, domain adaptive object detection and fusion architectures for 3D object detection.

MVX-Net: Multimodal VoxelNet for 3D Object Detection DAFE-FD: Density Aware Feature Enrichment for Face Detection Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results

My research interests lie in the intersection computer vision and machine learning algorithms for several practical applications such as object detection/counting, domain adaptation, image restoration, etc.

Face Synthesis

Face synthesis is crucial for several biometric applications such as face forensics, data augmentation for facial recognition algorithms. We design novel loss functions based on generative adversarial networks for translating between faces and sketches/landmarks.

Face synthesis is crucial for several biometric applications such as face forensics, data augmentation for facial recognition algorithms. We design novel loss functions based on generative adversarial networks for translating between faces and sketches/landmarks.

GP-GAN: Gender Preserving Gan for Synthesizing Faces from Landmarks High-quality Facial Photo-Sketch Synthesis using Multi-Adversarial Networks

Selected Publications

Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method

Vishwanath A. Sindagi, Rajeev Yasarla and Vishal M. Patel

IEEE International Conference on Computer Vision (ICCV) 2019, Seoul, South Korea.

Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

Vishwanath A. Sindagi and Vishal M. Patel

IEEE International Conference on Computer Vision (ICCV) 2019, Seoul, South Korea.

Image De-raining using a Conditional Generative Adversarial Network

He Zhang, Vishwanath A. Sindagi and Vishal M. Patel

IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 2019.

MVX-Net: Multimodal VoxelNet for 3D Object Detection

Vishwanath A. Sindagi, Yin Zhou and Oncel Tuzel

IEEE International Conference on Robotics and Automation (ICRA) 2019, Montreal, Canada.

GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

Xing Di, Vishwanath A. Sindagi and Vishal M. Patel

IEEE International Conference on Pattern Recognition (ICPR) 2018, Beijing, China.

Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs

Vishwanath A. Sindagi and Vishal M. Patel

IEEE International Conference on Computer Vision (ICCV) 2017, Venice, Italy.

All Publications